1. Seminar at the College of Education at Tohoku University, Sendai, Japan, July 25, 2014 Social Class and Educational Inequality in South Korea Kwang-Yeong Shin Chung-Ang University Seoul, Korea E-mail: kyshin@cau.ac.kr

2. Contents  Introduction  Korean Education in a Comparative Context  Social Class and Educational Inequality  Data and Methods  Results  Conclusion

3. Korean Education in a Comparative Perspective  Korean Context -The rate of high school students who go to the university is highest in the world. 85.6% in 2006. -Fierce Competition for University Entrance Exam -Extensive Development of Shadow Education (Private After-School Education)

4. Educational Institutions  General High Schools - White collar workers and the middle class 71.5%  Vocational High Schools - Supply of skilled workers for manufacturing industry28.5%  High Proportion of the Private Education - High School 47.1% - University 78.7%

6. The Expenditure for Higher Education in 2012

7. Social Class and Educational Inequality  Educational Competition -The lower class parents try to make their offspring move upwardly. -The upper class parents try to make their offspring not move downwardly. -Education became a site of silent class war in the contemporary Korean society.  The Poor State Welfare and Gradual Erosion of Family System  Intensification of the Educational Competition for Jobs

8. Persistent Educational Inequality  Class Reproduction (Bowles and Gintis 1976 and 2002; Breen and Goldthorpe 2002; Bourdieu 1983)  Intergenerational inheritance of inequality (Katz and Autor 1999, Bowles, Gintis and Groves eds. 2005)  “Family background (money and culture) is still important in getting ahead.”

9. Association between Class and Education  Class Matters? - If educational inequality persists, is it based on social class?  If there is, to what extent class matters? - Controlling the effects of other factors, is there a net class effect?  How does class matter?

10. Data and Methods  Data - Korea Education & Employment Panel (KEEP) (1-3 waves)(2004-2006) General High School: 2000 cases Vocational High School: 2000 cases  Methods - Log-linear Model - X2(Nested Model Tests) and BIC

11. Type of Children’s High School by Father’s Class

12. Transition after High School by High School Types

13. Choices of High School Graduates by Father’s Class

14. Log-Linear Model  logF(ijkl) = μ + μ e(i) + μ c(j) + μ h(k) + μ u(l) + μ ec(ij) + μ eh(ik) + μ eu(il) + μ ch(jk) + μ cu(jl) + μ hu(kl)+ μ ech(ijk)+ μ ecu(ijm) + μ ehu(ikl) + μ chu(jkl) + μ echu(ijkl)  Where ∑ μ e(i) =∑ μ c(j) = ∑ μ h(k) = ∑ μ u(l) = ∑ μ ec(ij) = μ eh(ik) =∑ μ eu(il) =∑ μ ch(jk) = ∑ μ cu(jl) =∑ μ hu(kl) = ∑ μ ech(ijk) = ∑ μ ecu(ijm) =∑ μ ehu(ikl) =∑ μ chu(jkl) = ∑ μ echu(ijkl) = 0.

15. A Model for Analysis Parental Generation Filial Generation Parent's Education (E) ↓ ↘ ↘ ↓ High School (H) → University (U) ↓ ↗ ↗ Parents' Class Position (C)

16. Odds and Odds Ratio  General HS. versus Vocational HS. Odds TotalMaleFemale -CAP= 1.64551.65981.6235 -PB =.929.9506.9048 -M = 3.7893.65223.0000 -WC =.8256.8497.8164 Odds Ratio TotalMaleFemale CAP : M =.434.4545.5412 PB: M =.245.2603.3010 WC: M =.217.2327.2721

17. (continued)  University or College versus High School Only Odds TotalMaleFemale -CAP= 1.1671.3455.9911 -PB=.8975.9506.8410 -M=1.71741.54762.000 -W=.75001.0275.6546 Odds Ratio CAP: M=.6797.8694.4955 PB: M =.5226.6142.4204 W: M =.4376.6639.3273

18. (continued)  University versus High School Only Odds TotalMaleFemale -CAP=2.40612.84622.0182 -PB=1.72142.15521.3767 -M=2.80472.36363.7037 -W=1.42581.68631.1720 Odds Ratio CAP: M =.85791.2042.5449 PB: M =.6138.9118.3717 W: M =.5084.7134.3164

19. (continued)  College versus High School Only Odds TotalMaleFemale -CAP=1.07481.11541.0364 -PB=.91601.2672.6370 -M=.6341.5273.8519 -W=.90001.0131.7898 Odd Ratio CAP: M = 1.69502.11531.2166 PB: M= 1.44462.4032.7477 W: M= 1.41931.9213.9271

20. Log-linear Models and Test Statistics =========================================================== Model G² df BIC --------------------------------------------------------------------------------------------------- (1) (E, C, H, U) 1549.79 63 1203.99 (2) (EC, H, U) 929.04 57 616.97 (3) (EC, CH, U) 755.48 54 442.61 (4) (EC, EH, U) 602.23 55 300.34 (5) (EC, CH, EH, U) 570.47 52 285.05 (6) (EC, CH, EH, HU) 162.25 50 -112.20 (7) (EC, CH, EH, HU, EU) 134.76 46 -106.75 (8) (EC, CH, EH, HU, CU) 147.44 44 -94.07 (9) (EC, CH, EH, HU, EU, CU) 125.06 40 -94.05 (10) (EC, CH, EH, HU, EU, CU, EHU) 54.64 36 -140.96 (11) (EC, CH, EH, HU, EU, CU, ECH) 121.17 34 -65.46 (12) (EC, CH, EH, HU, EU, CU, CHU) 76.78 34 -109.84 (13) (EC, CH, EH, HU, EU, CU, ECU) 100.19 28 -53.50 (14) (EC, CH, EH, HU, EU, CU, EHU, CHU) 29.47 30 -135.20 ----------------------------------------------------------

21. A Model for Analysis Parental Generation Filial Generation Parent's Education (E) ↓ ↘ ↘ ↓ High School (H) → University (U) ↓ ↗ ↗ Parents' Class Position (C)

22. Log-linear Models and Test Statistics (Boys) ============================================================== Model G² df BIC -------------------------------------------------------------------------------------------------------- (1) (E, C, H, U) 893.25 63 430.41 (2) (EC, H, U) 523.20 57 104.44 (3) (EC, CH, U) 450.58 54 143.86 (4) (EC, EH, U) 362.19 55 -41.88 (5) (EC, CH, EH, U) 345.60 52 -36.43 (6) (EC, CH, EH, HU) 108.61 50 -258.72 (7) (EC, CH, EH, HU, EU) 97.22 46 -240.73 (8) (EC, CH, EH, HU, CU) 99.34 44 -223.91 (9) (EC, CH, EH, HU, EU, CU) 90.71 40 -203.16 (10) (EC, CH, EH, HU, EU, CU, EHU) 56.75 36 -258.73 (11) (EC, CH, EH, HU, EU, CU, ECH) 85.81 34 -163.98 (12) (EC, CH, EH, HU, EU, CU, CHU) 54.77 34 -244.02 (13) (EC, CH, EH, HU, EU, CU, ECU) 70.29 28 -135.42 (14) (EC, CH, EH, HU, EU, CU, EHU, CHU) 31.94 30 -188.46 ------------------------------------------------------------------------------------------------------

23. Log-linear Models and Test Statistics (Girls) ================================================================= Model G² df BIC ------------------------------------------------------------------------------------------------------------- (1) (E, C, H, U) 743.55 63 293.20 (2) (EC, H, U) 481.68 57 74.22 (3) (EC, CH, U) 411.84 54 25.83 (4) (EC, EH, U) 326.17 55 -66.99 (5) (EC, CH, EH, U) 310.16 52 -61.55 (6) (EC, CH, EH, HU) 107.35 50 -172.94 (7) (EC, CH, EH, HU, EU) 88.97 46 -239.85 (8) (EC, CH, EH, HU, CU) 92.24 44 -222.29 (9) (EC, CH, EH, HU, EU, EHU) 54.19 42 -246.04 (10) (EC, CH, EH, HU, EU, CU) 76.23 40 -209.70 (11) (EC, CH, EH, HU, EU, CU, EHU) 40.24 36 -257.10 (12) (EC, CH, EH, HU, EU, CU, ECH) 73.01 34 -170.03 (13) (EC, CH, EH, HU, EU, CU, CHU) 53.88 34 -189.16 (14) (EC, CH, EH, HU, EU, CU, ECU) 64.05 28 -136.11 (15) (EC, CH, EH, HU, EU, CU, EHU, CHU) - - - ------------------------------------------------------------------------------------------------------------ Note) Model (14) is not testable due to some sampling zero cells.

24. Conclusion  Family background (class and education) significantly affects children’s educational achievement (the middle class thesis).  An effect of father’s class on children’s education can be observed at the early stage of education, the transition from secondary to tertiary education.  There is gender difference regarding effects of family background on children’s education in the late stage of educational transition from high school to university.

25. Conclusion (continued)  We might get more comprehensive understanding of educational inequality in Korea after several years as the Korea Education & Employment Panel (KEEP) survey proceeds.  With the rise of unemployment of the university graduates after the financial crisis, class differentials becomes larger in the university education. Wealthy families send their children abroad to learn foreign language for one or two years, especially English, that is crucial for job qualification in Korea.  We can not account students who study abroad from the early stage.  More than 1 percent of high school students went abroad to study.

26. Thank you very much.